Selected work

Representative Papers

Drug-Target Interaction Prediction via Hierarchical Sequential Cross-Attention over Chemical and Protein Language Models

Sep 28, 2026

This study addresses the limitation of existing drug-target interaction (DTI) models that independently encode sequences and struggle to explicitly model cross-molecular dependencies. Inspired by the induced-fit mechanism, we propose a bidirectional cross-attention framework. Methodologically, it integrates ChemBERTa and ESM-2 pretrained representations, employing hierarchical sequential cross-attention to enable fine-grained interactions between chemical substructures and protein regions. One-dimensional convolutions and attention pooling are then utilized to construct fixed-size interaction vectors. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on the BIOSNAP dataset and matches SOTA AUROC on the Davis dataset. Notably, with only 25.2M parameters, it maintains strong competitiveness while exhibiting superior cold-start generalization capabilities.

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Gated Spatial Redundancy Projection for Pathology Transformer Attentions

Aug 08, 2026

This work addresses the challenge posed by high spatial redundancy among neighboring patches in whole-slide pathological images, which can cause Transformer self-attention mechanisms to over-mix local features and obscure critical diagnostic signals. To mitigate this, the authors propose Gated SRP, a lightweight, plug-and-play module that estimates a local redundancy direction for each patch and attention head within the self-attention layer, projects the output onto this direction, and applies a learnable sign-gated geometric correction. This is the first attention modulation mechanism explicitly designed for the spatial redundancy inherent in histopathology images, introducing only a 0.02% parameter overhead while effectively preserving discriminative information. Experiments demonstrate that the method achieves the highest average C-index across five TCGA survival cohorts, outperforms baselines on 12 of 16 metrics across five slide-level classification datasets, and attains state-of-the-art AUC on three of them.

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Recent publications

Latest Papers

Drug-Target Interaction Prediction via Hierarchical Sequential Cross-Attention over Chemical and Protein Language Models

Sep 28, 2026

This study addresses the limitation of existing drug-target interaction (DTI) models that independently encode sequences and struggle to explicitly model cross-molecular dependencies. Inspired by the induced-fit mechanism, we propose a bidirectional cross-attention framework. Methodologically, it integrates ChemBERTa and ESM-2 pretrained representations, employing hierarchical sequential cross-attention to enable fine-grained interactions between chemical substructures and protein regions. One-dimensional convolutions and attention pooling are then utilized to construct fixed-size interaction vectors. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on the BIOSNAP dataset and matches SOTA AUROC on the Davis dataset. Notably, with only 25.2M parameters, it maintains strong competitiveness while exhibiting superior cold-start generalization capabilities.

0 citationsRead paper

Gated Spatial Redundancy Projection for Pathology Transformer Attentions

Aug 08, 2026

This work addresses the challenge posed by high spatial redundancy among neighboring patches in whole-slide pathological images, which can cause Transformer self-attention mechanisms to over-mix local features and obscure critical diagnostic signals. To mitigate this, the authors propose Gated SRP, a lightweight, plug-and-play module that estimates a local redundancy direction for each patch and attention head within the self-attention layer, projects the output onto this direction, and applies a learnable sign-gated geometric correction. This is the first attention modulation mechanism explicitly designed for the spatial redundancy inherent in histopathology images, introducing only a 0.02% parameter overhead while effectively preserving discriminative information. Experiments demonstrate that the method achieves the highest average C-index across five TCGA survival cohorts, outperforms baselines on 12 of 16 metrics across five slide-level classification datasets, and attains state-of-the-art AUC on three of them.

0 citationsRead paper